A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural Network

Benedikt Fesl, Nurettin Turan, Michael Koller, Wolfgang Utschick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A convolutional neural network estimator which learns the minimum mean squared error channel estimator for single-antenna users was recently proposed. We generalize the architecture to the estimation of MIMO channels with multiple-antenna users and incorporate assumptions, such as a high number of antennas and a single propagation cluster, which reduces the computational complexity tremendously. Learning is used in this context to combat the mismatch between the assumptions and real scenarios with a limited number of antennas and many propagation clusters. We derive a high-level description of the estimator for arbitrary choices of the pilot sequence. It turns out that the proposed estimator has the implicit structure of a two-layered convolutional neural network, where the derived quantities can be relaxed to learnable parameters. We show that by using discrete Fourier transform based pilots the number of network parameters decreases significantly and the online run time of the estimator is reduced considerably, where we can achieve linearithmic order of complexity in the number of antennas. Numerical results display performance gains compared to state-of-the-art algorithms from the field of compressive sensing or covariance estimation of the same or higher computational complexity. The simulation code is available online.

Original languageEnglish
Title of host publication2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11-15
Number of pages5
ISBN (Electronic)9781665428514
DOIs
StatePublished - 2021
Event22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 - Lucca, Italy
Duration: 27 Sep 202130 Sep 2021

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2021-September

Conference

Conference22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
Country/TerritoryItaly
CityLucca
Period27/09/2130/09/21

Keywords

  • Channel estimation
  • machine learning
  • massive MIMO
  • neural networks
  • spatial channel model

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